Each retained superpixel region can serve as input to a ConvNet in order to assign a more distinct probability \(p_\mathrm {ConvNet}\)  of each superpixel in \(S_\mathrm {RF}\)  as being pancreas. Our ConvNet samples the bounding box of each superpixel \(S_\mathrm {RF}^i\)  at different scales \(s\)  and random non-rigid deformations \(t\)  (at training time). The degree of deformation is chosen such that the resulting warped images resemble plausible physical variations of the medical images. This approach is commonly referred to as data augmentation and can help avoid overfitting [1]}, [2]}.
